Description Usage Arguments Value Author(s) References See Also Examples
The estimated coefficients at level i are obtained by adding together the fixed effects estimates and the corresponding random effects estimates at grouping levels less or equal to i. The resulting estimates are returned as a data frame, with rows corresponding to groups and columns to coefficients. Optionally, the returned data frame may be augmented with covariates summarized over groups.
1 2 3 |
object |
an object inheriting from class |
augFrame |
an optional logical value. If |
level |
an optional positive integer giving the level of grouping to be used in extracting the coefficients from an object with multiple nested grouping levels. Defaults to the highest or innermost level of grouping. |
data |
an optional data frame with the variables to be used for
augmenting the returned data frame when |
which |
an optional positive integer or character vector
specifying which columns of |
FUN |
an optional summary function or a list of summary functions
to be applied to group-varying variables, when collapsing |
omitGroupingFactor |
an optional logical value. When |
subset |
an optional expression specifying a subset |
... |
some methods for this generic require additional arguments. None are used in this method. |
a data frame inheriting from class "coef.lme"
with the estimated
coefficients at level level
and, optionally, other covariates
summarized over groups. The returned object also inherits from classes
"ranef.lme"
and "data.frame"
.
José Pinheiro and Douglas Bates bates@stat.wisc.edu
Pinheiro, J. C. and Bates, D. M. (2000), Mixed-Effects Models in S and S-PLUS, Springer, New York, esp. pp. 455-457.
lme
,
ranef.lme
,
plot.ranef.lme
, gsummary
1 2 3 |
(Intercept) age
M16 16.57335 0.5913314
M05 15.58444 0.6857855
M02 16.03361 0.6746930
M11 17.65160 0.5413593
M07 16.15314 0.6950852
M08 17.62141 0.5654490
M03 16.58721 0.6960375
M12 15.76312 0.7747492
M13 12.63157 1.0738537
M14 17.66546 0.6460654
M09 16.31672 0.7960938
M15 16.22614 0.8683628
M06 17.97875 0.7433765
M04 19.76156 0.5943004
M01 17.81269 0.8758697
M10 19.41435 0.8713318
F10 14.47973 0.4095945
F09 16.47016 0.4421435
F06 16.14053 0.4736282
F01 16.27515 0.4819755
F05 17.27792 0.4922276
F07 16.57335 0.5913314
F02 15.74926 0.6700431
F08 18.01143 0.4857849
F03 15.98832 0.7108275
F04 17.83027 0.6303230
F11 17.97875 0.7433765
(Intercept) age distance Sex
M16 16.57335 0.5913314 23.000 Male
M05 15.58444 0.6857855 23.000 Male
M02 16.03361 0.6746930 23.375 Male
M11 17.65160 0.5413593 23.625 Male
M07 16.15314 0.6950852 23.750 Male
M08 17.62141 0.5654490 23.875 Male
M03 16.58721 0.6960375 24.250 Male
M12 15.76312 0.7747492 24.250 Male
M13 12.63157 1.0738537 24.250 Male
M14 17.66546 0.6460654 24.875 Male
M09 16.31672 0.7960938 25.125 Male
M15 16.22614 0.8683628 25.875 Male
M06 17.97875 0.7433765 26.375 Male
M04 19.76156 0.5943004 26.625 Male
M01 17.81269 0.8758697 27.750 Male
M10 19.41435 0.8713318 29.500 Male
F10 14.47973 0.4095945 18.500 Female
F09 16.47016 0.4421435 21.125 Female
F06 16.14053 0.4736282 21.125 Female
F01 16.27515 0.4819755 21.375 Female
F05 17.27792 0.4922276 22.625 Female
F07 16.57335 0.5913314 23.000 Female
F02 15.74926 0.6700431 23.000 Female
F08 18.01143 0.4857849 23.375 Female
F03 15.98832 0.7108275 23.750 Female
F04 17.83027 0.6303230 24.875 Female
F11 17.97875 0.7433765 26.375 Female
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